K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels su...

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Bibliographic Details
Published in:Scientific data 2023-06, Vol.10 (1), p.351-351, Article 351
Main Authors: Kang, Soowon, Choi, Woohyeok, Park, Cheul Young, Cha, Narae, Kim, Auk, Khandoker, Ahsan Habib, Hadjileontiadis, Leontios, Kim, Heepyung, Jeong, Yong, Lee, Uichin
Format: Article
Language:English
Subjects:
631/443
631/477/2811
706/648/697/129
Attention
Attention task
Cognitive ability
Computer engineering
Data Descriptor
Datasets
Emotional behavior
Emotions
Humanities and Social Sciences
Humans
multidisciplinary
Physiology
Science
Science (multidisciplinary)
Self Report
Smartphone
Smartphones
Wearable Electronic Devices
Well being
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Summary:With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone , a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02248-2